This form of machine learning, which falls between unsupervised learning and supervised learning, uses a minimal quantity of annotated (labeled) data and a large amount of un-annotated (unlabelled) data during training [1]}. The presence of the unlabelled datasets and the open access to the unlabelled datasets is the feature that differentiates the semi-supervised from supervised learning. [2]} employ a semi-supervised learning approach for automatic sarcasm identification using two different forms of text, tweets from Twitter, and product reviews from Amazon. A total number of 66,000 products and book reviews are collected in their study, and both syntactic and pattern-based features are extracted. The sentiment polarity of 1 to 5 is chosen on the training phase for each training data. The authors report a performance of %77 precision.
